A New Paradigm for Illness Monitoring and Relapse Prevention in Schizophrenia

描述（申请人提供）：精神分裂症是一种严重的精神疾病，与惊人的高个人和社会成本。虽然这种疾病通常是慢性的，但它不是静态的，大多数精神分裂症患者在完全或部分缓解和症状复发之间摇摆不定。复发会增加一个人面临重大问题的风险，包括无家可归、监禁、受害和自杀。此外，复发的精神分裂症患者的成本是未复发患者的三到四倍。该项目的目标是开发和评估一种新的模式，用于精神分裂症的疾病监测，早期预警信号的检测和复发预防。我们的临床研究人员和计算机科学家的跨学科团队建议开发一种移动的系统，该系统使用智能手机嵌入式传感器（即麦克风，加速度计，GPS，光传感器）加上计算机化的自我报告，以跟踪与精神分裂症复发相关的一系列行为（即言语，身体活动，位置，睡眠，情绪，精神病症状的语言方面）。利用机器学习技术，该系统将利用行为数据和患者自我报告的临床更新来生成个性化的预警模型。这些模型将随着系统的使用而随着时间的推移而发展，重点关注一个人的典型行为模式的可变性，以校准独特的患者复发特征。治疗团队将通过安全网站了解患者的临床状况。当移动的系统“标记”与某人的复发特征一致的趋势时，它将触发患者功能和提供者功能（即，实时通知、启动联系的提示、时间敏感的治疗）以帮助防止进展为完全精神病复发。在该项目的第一阶段，我们将把多模态传感器、生态瞬时评估和机器学习技术集成到一个统一的智能手机系统中，并在实验室环境中进行测试和改进。在第二阶段，我们将在现实世界条件下对精神分裂症患者进行现场试验，以识别和解决技术和机械问题，调整软件，并最大限度地提高系统的可用性。在第三阶段，我们将在150名复发风险高的精神分裂症门诊患者中进行一项为期12个月的随机试验，将监测和预防系统与常规治疗进行比较。如果成功，我们提出的系统可以迅速提供给迫切需要更有效的资源的人群，并可以作为一个模板，移动的监测和治疗系统的一系列临床条件与情节的性质。

DESCRIPTION (provided by applicant): Schizophrenia is a severe psychiatric disorder that is associated with staggeringly high individual and societal costs. Although the illness is typically chronic, it is not static, and the majority of people with schizophrenia vacillate between full or partial remission and episodes of symptomatic relapse. Relapses increase one's risk for major problems including homelessness, incarceration, victimization, and suicide. Moreover, patients with schizophrenia who relapse are three to four times more costly than those who do not. The goal of the proposed project is to develop and evaluate a novel paradigm for illness monitoring, detection of early warning signs, and relapse prevention in schizophrenia. Our interdisciplinary team of clinical researchers and computer scientists proposes to develop a mobile system that uses smartphone-embedded sensors (i.e. microphone, accelerometer, GPS, light sensor) coupled with computerized self-reports, to track a range of behaviors (i.e. paralinguistic aspect of speech, physical activity, location, sleep, mood, psychotic symptoms) that are relevant to relapse in schizophrenia. Using machine learning techniques, the system will leverage behavioral data and patient self-reported clinical updates to generate personalized early warning models. The models will evolve with use of the system over time, focusing on variability from one's typical behavioral patterns to calibrate a unique patient relapse signature. Treatment teams will be informed about patients' clinical status via secure website. When the mobile system "flags" trends that are consistent with one's relapse signature, it will trigger patient functions and provider functions (i.e. real-time notification, prompts to initiate contact, time-sensitive treatments) to help prevent progression to full psychotic relapse. In Phase 1 of the project, we will integrate multi-modal sensor, ecological momentary assessment, and machine learning technologies into a unified smartphone system that will be tested and refined in laboratory settings. In Phase 2, we will conduct field trials with individuals with schizophrenia i real-world conditions to identify and resolve technical and mechanical problems, adapt the software, and maximize system usability. In Phase 3, we will conduct a randomized 12- month trial of the monitoring and prevention system compared to treatment as usual in 150 outpatients with schizophrenia that are at high-risk for relapse. If successful, our proposed system can be rapidly made available to a population that is in dire need of more effective resources, and can serve as a template for mobile monitoring and treatment systems for a range of clinical conditions with an episodic nature.